Postdoc: Causal AI & Foundation Models for Discovery

Commonwealth of VA Careers; Charlottesville (VA); On-site; USD 55,000 - 65,000; Full time

Benefits offered by this job

Health plan

Vision coverage

Dental plan

Paid time off 22 days per year

8 weeks parental leave

Job summary

The University of Virginia School of Data Science invites applications for a Postdoctoral Research Associate at the intersection of foundation models and causal inference. The successful candidate will pursue a bidirectional research agenda, developing algorithms, benchmarks, and evaluation methods while collaborating with the RISE Lab and partners across UVA and external institutions.

Reporting to Sheng Li, you will mentor graduate students, publish in top venues, and contribute to

Qualifications

• Doctoral degree in Data Science, Computer Science, ML, Statistics, Electrical and Computer Engineering, Information Science, or related field.
• Strong publication record commensurate with experience.
• Foundation models, LLMs, multimodal learning, NLP, generative AI, or deep learning; or causal inference, causal discovery, causal ML, graphical models, experimental design, or related statistics.
• Experience designing computational research, analyzing results, and communicating findings.
• Ability to lead research projects with faculty guidance while collaborating in a team.
• Strong written and oral communication skills.
• Commitment to rigorous, reproducible, and ethical research practices.

Responsibilities

• Lead independent and collaborative research projects involving foundation models, causal inference, causal discovery, causal ML, and AI-enabled scientific discovery.
• Formulate research questions, develop novel methods and algorithms, and design rigorous computational experiments.
• Investigate how foundation models can incorporate domain knowledge to generate, refine, and evaluate causal hypotheses.
• Develop causal methods to improve reasoning, trustworthiness, interpretability, robustness, safety, and generalizability of foundation models.
• Develop benchmarks, datasets, evaluation protocols, and reproducible research software.
• Prepare high-quality manuscripts for peer-reviewed venues.
• Mentor graduate students and provide guidance on research design, implementation, and writing.
• Collaborate across data science, CS, stats, health, education, and other disciplines.
• Contribute to proposals, open-source software, and scholarly products; uphold research integrity and ethical use of data.

Skills

Foundation models exploration

Causal inference

Research leadership

Scientific communication

Computational experiments

Education

PhD in Data Science or related field

Tools

GPU computing

Distributed training

Job description

The University of Virginia School of Data Science invites applications for a Postdoctoral Research Associate at the intersection of foundation models and causal inference. The successful candidate will pursue a bidirectional research agenda, developing algorithms, benchmarks, and evaluation methods while collaborating with the RISE Lab and partners across UVA and external institutions.

Reporting to Sheng Li, you will mentor graduate students, publish in top venues, and contribute to